SlowGuess/ACL_2020_OCR huggingface dataset:
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Evaluating Redundant Annotations Recent datasets and parser outputs of QA-SRL (Fitzgerald et al., 2018) produce redundant arguments. On the other hand, our consolidated gold data, as typical, consists of a single non-redundant annotation, where arguments are non-overlapping. In order to fairly evaluate such redundant annotations against our gold standard, we ignore predicted arguments that match ground-truth but are not selected by the bipartite matching due to redundancy. After connecting unmatched predicted arguments that overlap, we count one false positive for every connected component, aiming to avoid penalizing precision too harshly when predictions are redundant.
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